DATASETS FOR CASE STUDIES - MS Excel, MS PowerBI, Python, R and other Analytical visualization tools.

📂 DATASETS FOR CASE STUDIES

Here are the datasets for the case studies and hands-on activities. These datasets are structured in text format for easy use with Power BI, Excel, and Python. You can connect this blog link to the application and access all the tables or copy and paste each table as required.

1️Sales Performance Analysis (Retail Chain)

Date	Store	Product	Category	Revenue	Profit	Units Sold
2024-01-05	Store A	Laptop	Electronics	85000	12000	5
2024-02-10	Store B	Mobile	Electronics	55000	8000	10
2024-03-15	Store A	Shoes	Fashion	12000	3000	20
2024-04-20	Store C	TV	Electronics	95000	15000	4
2024-05-25	Store B	Sofa	Furniture	67000	10000	3

📌 Columns: Date, Store, Product, Category, Revenue, Profit, Units Sold
🎯 Use Cases: Bar charts (store-wise sales), Line charts (sales trends), Tree maps (top products)

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2️Employee Attrition (IT Company)

Employee ID	Age	Department	Salary	Satisfaction Score	Attrition
E101	25	IT	50000	3.5	Yes
E102	30	HR	60000	4.2	No
E103	27	Sales	55000	3.0	Yes
E104	35	IT	70000	4.8	No
E105	40	Marketing	65000	4.0	Yes

📌 Columns: Employee ID, Age, Department, Salary, Satisfaction Score, Attrition (Yes/No)
🎯 Use Cases: Pie charts (attrition rate), Scatter plots (salary vs attrition), Machine learning (predict attrition)

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3️Customer Segmentation (Banking)

Customer ID	Age	Income	Credit Score	Spending Score	Account Balance
C201	24	30000	650	45	20000
C202	45	70000	780	80	100000
C203	33	45000	720	60	50000
C204	50	85000	800	90	150000
C205	29	40000	690	55	25000

📌 Columns: Customer ID, Age, Income, Credit Score, Spending Score, Account Balance
🎯 Use Cases: Clustering (K-Means in Python), Scatter plots (customer segmentation), Box plots (spending behavior)

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4️Credit Card Fraud Detection

Transaction ID	Amount	Location	Customer ID	Fraudulent
T5001	5000	New York	C101	No
T5002	12000	London	C102	Yes
T5003	4500	Mumbai	C103	No
T5004	15000	Paris	C104	Yes
T5005	7000	Tokyo	C105	No

📌 Columns: Transaction ID, Amount, Location, Customer ID, Fraudulent (Yes/No)
🎯 Use Cases: Box plots (outlier detection), Decision Trees (fraud classification), Heat maps (fraud hotspots)

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5️Student Performance Prediction

Student ID	Attendance (%)	Study Hours	Previous Grades	Final Score
S101	90	6	85	88
S102	75	4	70	72
S103	80	5	78	79
S104	95	7	90	92
S105	60	3	65	68

📌 Columns: Student ID, Attendance (%), Study Hours, Previous Grades, Final Score
🎯 Use Cases: Correlation analysis (attendance vs performance), Regression (predict final score)

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6️Air Pollution Trends

Date	City	PM2.5 Level	Temperature	Health Cases Reported
2024-01-01	Delhi	180	15	120
2024-02-01	Beijing	220	10	200
2024-03-01	New York	90	18	50
2024-04-01	London	70	12	30
2024-05-01	Tokyo	110	20	75

📌 Columns: Date, City, PM2.5 Level, Temperature, Health Cases Reported
🎯 Use Cases: Time series forecasting (pollution trends), Heat maps (pollution hotspots)

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7Waterfall Chart Dataset – This dataset represents a company's monthly profit & loss statement, showing revenue, expenses, and net profit/loss changes over time.

Category	Amount
Revenue	50000
COGS	-20000
Gross Profit	30000
Operating Expenses	-10000
Net Profit	20000

8 Standard Deviation Dataset – This dataset includes students' test scores across multiple subjects, allowing you to calculate the mean, variance, and standard deviation.

Student	Math	Science	English
Student 1	78	82	74
Student 2	85	79	80
Student 3	92	91	85
Student 4	88	87	78
Student 5	76	85	82
Student 6	81	90	88
Student 7	95	94	91
Student 8	89	83	76
Student 9	84	88	84
Student 10	91	86	79

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